The National Institutes of Health’s (NIH) National Heart, Lung, and Blood Institute (NHLBI) and the Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD), with participation from the Foundation for NIH Biomarker Consortium hosted a joint virtual workshop on February 9th and 12th, 2021, on prediction modeling for adverse pregnancy outcomes (APOs) associated with maternal morbidity and mortality and subsequent risk for cardiovascular disease (CVD). The main goals of the workshop were threefold. First, to explore whether a risk prediction model or tool could be developed to inform risk for APO. Second, to determine whether a risk prediction model or tool can predict future CVD in women of childbearing age with and without a history of APO. And third, if the answer is yes to either research question, what do we need to do to accomplish these objectives? For example, what additional data are needed and how can information from different cohorts be combined? Or if we are not currently able to develop such risk prediction models and tools, what barriers must be overcome to develop those capabilities? How can we apply lessons learned from other disciplines’ successes and failures?
The purpose of this workshop was to identify initial steps towards the development of a risk prediction model and potential tool for maternal morbidity and mortality and CVD in young women. Most risk algorithms heavily rely on age and therefore are not useful in younger women, particularly during childbearing years. In addition, models or tools need to be developed that focus our ability to address the persistent racial and ethnic disparities noted in maternal morbidity and CVD. Our expectation was that by assembling a multidisciplinary group of experts (including clinicians, managers of large pregnancy registries, and biomedical informaticians), we could identify key research gaps, and propose strategies to develop an initial risk prediction tool. Such a tool could be used for both clinical management and research, with the ultimate goal of altering the trajectories of CVD in young women, particularly for those who have experienced APOs.
APOs (such as preeclampsia, eclampsia, peripartum cardiomyopathy, preterm birth, gestational diabetes) and preexisting conditions (asthma, sleep disordered breathing, obesity, chronic hypertension, diabetes, social determinants of health) are associated with an increased risk of later CVD. However, the positive predictive value of currently available measures is poor. More precise risk stratification is critical to prognosticating CVD risk in young women during their childbearing years, particularly among women of color, in order to identify who would most benefit and how interventions can be applied to reduce increased risk of CVD outcomes. In addition to traditional risk factors (age, race/ethnicity, obesity status, blood pressure and lipids), genetic and biomarker data may help to create risk algorithms for these young women. Biomarkers may be particularly useful for nulliparous women with no apparent risk factors.
Combinations of clinical and genetic data, biomarkers assessed via machine learning, artificial neural networks, and systems biology have the potential to dramatically improve our ability to identify women at risk for CVD compared to single factors. The appropriate combinations of risk factors may be difficult to recognize through traditional statistical approaches. Fortunately, the field of biomedical informatics has developed novel analytic approaches that can analyze the vast quantities of data generated by omics technologies. Complex analyses that use millions of data points require large data sets, clinical data from electronic health records, biosample banks, and well characterized phenotypes in order to be successful. Combining datasets is especially important to obtain adequate racial and ethnic diversity in sufficient numbers. In turn, this will facilitate our ability to address the persistent disparities noted in maternal morbidity and CVD in women.
General Principles for Risk Prediction Models
Variables and Endpoints Important for the Models
Sources of Data for the Models
There are several large pregnancy registries with data and/or biosamples that could be leveraged to perform discovery and subsequent validation for a model. The Global Pregnancy Collaboration provides access to high-quality data and biospecimens from 40 cohorts around the world. The nuMoM2b Heart Health Study is a highly-phenotyped cohort of 4500 women that defined the incidence of hypertension and the CVD risk profile of women 2-7 years after a first pregnancy. Preconception to pOst-partum study of cardiometabolic health in Primigravid PregnancY (or POPPY), a pre-conception cohort study, examined different models of life course trajectories of cardiometabolic dysfunction in healthy versus unhealthy pregnancies in nulliparous women. Pregnancy Outcome Prediction Study (POPS) is a longitudinal pregnancy biobank, with data, including maternal blood and urine at multiple time points, on well-phenotyped, physician-validated pregnancy outcomes. LifeCodes is a biobank with similar data on 3,365 pregnancies, from 2009 to present. Kaiser Permanente Northern California incorporates data elements including demographics, behavioral measures, and clinical encounters, as well as longitudinal data on both pregnancy and CVD.
Other registries to consider include the Nurses’ Health Study 2 and 3; Black Women’s Health Study; Study of Latinos: Hispanic Community Health Study; Growing Up Today Study; the Women’s Health Initiative; and the Child Health Development Studies. The best data are research data that must be harmonized to provide the strongest CVD prediction modeling possible.
Identification of Outstanding Opportunities/Gaps
In the final breakout session, groups were charged with proposing a model, based on the information heard over the previous day and a half. The following opportunities were identified:
Several robust pregnancy registries exist that could form the basis for modeling efforts. Multidisciplinary teams of clinicians, registry/sample owners, and bioinformaticians are essential to begin to develop potential models. A simpler “implementation” model based on existing knowledge may be the first attempt, later incorporating biological discovery using -omics data to identify pathways to APOs, which can ultimately lead to a clinical prediction model. A clinical prediction tool may be ideal for use at the 6-week post-partum visit, where assessment of CVD risk should happen and the patient-provider conversation about those risks should take place.
Janet Catov, Ph.D. (Co-Chair), University of Pittsburgh
Brian Cox, Ph.D. (Co-Chair), University of Toronto
Abi Fraser, Ph.D., M.P.H., University of Bristol, UK
Kathryn Gray, M.D., Ph.D., Brigham and Women’s Hospital (BWH)
Casey Greene, Ph.D., University of Colorado
David Haas, M.D., Indiana University
Ricardo Henao, Ph.D., Duke University
S. Ananth Karumanchi, M.D., Cedars-Sinai Hospital
Sadiya Khan, M.D., Northwestern University
Amit Khera, M.D., MIT/Massachusetts General Hospital
Judette Louis, M.D., M.P.H., University of Florida
Douglas McNair, M.D., Ph.D., Bill and Melinda Gates Foundation
Les Myatt, Ph.D., FRCOG, Oregon State Health University
Michael Pencina, Ph.D., Duke University
Janet Rich-Edwards, Sc.D., M.P.H., BWH/Harvard University
Jim Roberts, M.D., University of Pittsburgh
Laritza Rodriguez, M.D., National Library of Medicine
Graeme Smith, M.D., Ph.D., FRCSC, Queen’s University, Ontario
Jennifer Stuart, Sc.D., BWH/Harvard University
Viola Vaccarino, M.D., Ph.D., Emory University
Yeyi Zhu, Ph.D., Kaiser Permanente
Nahida Chakhtoura, M.D., NICHD
Jamie Gulin, M.P.H., NHLBI
John Ilekis, Ph.D., NICHD
Aaron Laposky, Ph.D., NHLBI
Megan Mitchell, M.P.H., NHLBI
Aaron Pawlyk, Ph.D., NICHD
Victoria Pemberton, RNC, M.S., CCRC, NHLBI
Rebecca Roper, M.S., M.P.H., NHLBI
Jasmina Varagic, M.D., Ph.D., NHLBI
Can we develop a risk prediction tool that is accurate and can inform risk for MMM (adverse pregnancy outcomes)? Can we develop a risk prediction tool for future CV disease in young women? If yes, what do we need to accomplish this? If no, what barriers must be overcome to develop these tools?
Rapid fire talks on successes and failures in prediction models
3 Multidisciplinary groups
Rapid fire talks on modeling techniques, successes, and failures
3 Multidisciplinary groups
Rapid fire talks on variables, risk factors, biomarkers and endpoints
3 Multidisciplinary groups
Summary of meeting goals
Discussion of potential approaches and the modeling processes
3 Multidisciplinary groups